Evidence map›Paper›PMID 39470971›Full record

SynthesisIrish journal of medical science2025

The value of lncRNAs as a biomarker for the pregnancy outcomes of gestational diabetes: a meta-analysis.

Chunling Bao, Jun Kuang, Chuanyong He, Hui Xu

Abstract readMeta-AnalysisReview
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In one paragraph

Synthesis in Irish journal of medical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chunling Bao *Department of Obstetrics and Gynecology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, No.158, Wuyang Avenue, Enshi, 445000, China.
Jun Kuang *Department of Obstetrics and Gynecology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, No.158, Wuyang Avenue, Enshi, 445000, China.
Chuanyong HeDepartment of Obstetrics and Gynecology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, No.158, Wuyang Avenue, Enshi, 445000, China.
Hui XuDepartment of Obstetrics and Gynecology, The Central Hospital of Enshi Tujia and Miao Autonomous Prefecture, No.158, Wuyang Avenue, Enshi, 445000, China. hbxfxuhui1986@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong non-coding RNAs (lncRNAs) are tissue-specific and highly stable and may serve as early molecular markers of adverse pregnancy outcomes in patients with gestational diabetes mellitus (GDM).

aimsThis study aimed to investigate the relationship between aberrant expression of lncRNAs and adverse pregnancy outcomes in GDM patients.

methodsWe searched electronic databases: Embase, Medline, EBSCO, PubMed, CNKI, and Wanfang databases to find relevant articles. Included studies quality was assessed using the QUADAS-2 quality assessment tool. Prognostic value of lncRNAs were pooled using risk ratio (RR) with 95% confidence interval (95%CI). The heterogeneity was analyzed using the Cochran Q test and I

resultsThe review included nine studies. Overall, the review found that altered expression of lncRNAs was associated with adverse pregnancy outcomes of GDM patients (RR: 1.616, 95%CI: 1.308-1.996), and was a risk factor for adverse pregnancy outcomes both of Infants (RR: 1.68, 95% CI: 1.30-2.17) and maternal (RR: 4.14, 95% CI: 1.90-9.03) in patients with GDM. The diagnostic procedure, diagnostic criteria, expression trend of lncRNAs, and adverse pregnancy outcomes were the sources of heterogeneity.

conclusionsAberrant expression of lncRNAs was strongly associated with the adverse pregnancy outcomes in GDM.

Indexed as

Diabetes, GestationalPregnancy OutcomeRNA, Long NoncodingBiomarkersFemaleHumansPregnancyBiomarkersRNA, Long NoncodingAdverse pregnancy outcomesGDMLncRNAsPrognosis

Identifiers

PMID39470971

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.